Fig. REF  shows the overall IMC aware binary neural network model for KWS. The binary neural network is selected because the multibit ADC required for IMC could be simplified as a low area cost and low power sense amplifier (SA) when combined with in-memory BN [1]}. The input is 8-bit raw audio data of nearly one second length. The model outputs the class of the input keyword. The model consists of one binarized sinc convolution layer [2]}, which is a learned filter bank to process raw audio. Compared to conventional MFCC, the computational complexity will be lower, and this also makes the model an end-to-end learning model. Following the filter banks, five binary convolution layers are adopted that use group convolution with group size set to 24 and in-memory BN as the basic block. With in-memory BN, convolution and BN can be executed together within the array, and the array output can be a binary activation output that can be implemented by SAs instead of multibit ADCs. The weights of the sinc convolution layers are also binary for hardware consideration. Thus, the model uses only binary computation in the convolution layer in inference, while the final classifier layer uses 8-bit fixed-point computation.
